Computer Science > Machine Learning

Title:
Snorkel: Rapid Training Data Creation with Weak Supervision

Abstract: Labeling training data is increasingly the largest bottleneck in deploying
machine learning systems. We present Snorkel, a first-of-its-kind system that
enables users to train state-of-the-art models without hand labeling any
training data. Instead, users write labeling functions that express arbitrary
heuristics, which can have unknown accuracies and correlations. Snorkel
denoises their outputs without access to ground truth by incorporating the
first end-to-end implementation of our recently proposed machine learning
paradigm, data programming. We present a flexible interface layer for writing
labeling functions based on our experience over the past year collaborating
with companies, agencies, and research labs. In a user study, subject matter
experts build models 2.8x faster and increase predictive performance an average
45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in
this new setting and propose an optimizer for automating tradeoff decisions
that gives up to 1.8x speedup per pipeline execution. In two collaborations,
with the U.S. Department of Veterans Affairs and the U.S. Food and Drug
Administration, and on four open-source text and image data sets representative
of other deployments, Snorkel provides 132% average improvements to predictive
performance over prior heuristic approaches and comes within an average 3.60%
of the predictive performance of large hand-curated training sets.